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Northeastern China is a region of high tick abundance, multiple tick-borne pathogens and likely human infections. The spectrum of diseases caused by tick-borne pathogens has not been objectively evaluated in this region for clinical management and for comparison with other regions globally where tick-transmitted diseases are common. Based on clinical symptoms, PCR, indirect immunofluorescent assay and (or) blood smear, we identified and described tick-borne diseases from patients with recent tick bite seen at Mudanjiang Forestry Central Hospital. From May 2010 to September 2011, 42% (75/180) of patients were diagnosed with a specific tick-borne disease, including Lyme borreliosis, tick-borne encephalitis, human granulocytic anaplasmosis, human babesiosis and spotted fever group rickettsiosis. When we compared clinical and laboratory features to identify factors that might discriminate tick-transmitted infections from those lacking that evidence, we revealed that erythema migrans and neurological manifestations were statistically significantly differently presented between those with and without documented aetiologies (P < 0.001, P = 0.003). Twelve patients (6.7%, 12/180) were co-infected with two tick-borne pathogens. We demonstrated the poor ability of clinicians to identify the specific tick-borne disease. In addition, it is necessary to develop specific laboratory assays for optimal diagnosis of tick-borne diseases.

Manipulating the thermal conductivity of solids is important for practical applications. Due to the fact that phonons in thermoelectric materials have longer mean free paths (MFPs) than electrons, strengthening phonon scattering to reduce lattice thermal conductivity (κlat) becomes the most straightforward and effective approach to enhance the thermoelectric figure of merit, ZT, which determines the maximum device efficiency. Phonons have a wide range of MFPs in semiconductors, and different dimensions of lattice defects can be targeted to scatter particular phonons with distinct relaxation times. Designing hierarchical nano-microstructures, spanning from point defects to volume defects, would be beneficial to achieve low κlat via a full spectrum of phonon scattering. Herein, we review the formation and underlying mechanisms for lattice defects and highlight the role of all-scale hierarchical nano-microstructure on phonon engineering. Existing challenges in simulations are also discussed.

It is a challenging task to discover information from a large amount of data in an open domain.1 In this paper, an event network framework is proposed to address this challenge. It is in fact an empirical construct for exploring open information, composed of three steps: document event detection, event network construction and event network analysis. First, documents are clustered into document events for reducing the impact of noisy and heterogeneous resources. Secondly, linguistic units (e.g., named entities or entity relations) are extracted from each document event and combined into an event network, which enables content-oriented retrieval. Then, in the final step, techniques such as social network or complex network can be applied to analyze the event network for exploring open information. In the implementation section, we provide examples of exploring open information via event network.

Freedom from fear and freedom from want are two of the fundamental freedoms and likely related to changes in the environment. It has usually been assumed that our subjective feelings should change accordingly with changes in the objective environment. However, two counterintuitive effects reviewed in this article imply a rather complex psychological mechanism behind how people respond to environmental changes and strive for the freedom from fear and want. The first is the ‘psychological typhoon eye’ effect, in which the closer people are to hazards, the calmer they feel. Several possible explanations have been proposed, but the mechanism behind this effect remains unclear. The findings are important for future post-disaster interventions and helpful for policy makers in risk management and researchers in risk studies. The second effect is the ‘town dislocation’ effect, wherein although inhabitants’ objective quality of life is improved during the urbanisation process, the projected endorsement and rated social ambience of town residents is lower than that of residents in the country and in the city; this effect is mediated by social support. The findings have implications for how to better assess the urbanisation process and how to improve people's affective appraisals of their living environment.

We present results of a regional comparative study of surface mass changes from 2004 to 2008 based on Gravity Recovery and Climate Experiment (GRACE), The Ice, Cloud and Land Elevation Satellite (ICESat) and CHINARE observations over the Lambert Glacier/Amery Ice Shelf system (LAS). Estimation of the ICESat mass change rates benefitted from the density measurements along the CHINARE traverse and a spatial density adjustment method for reducing the effect of spatial density variations. In the high-elevation inland region, a positive trend was estimated from both ICESat and GRACE data, which is in line with the CHINARE accumulation measurements. In the coastal region, there were areas with high level accumulations in both ICESat and GRACE trend maps. In many high flow-speed glacier areas, negative mass change rates may be caused by dynamic ice flow discharges that have surpassed the snow accumulation. Overall, the mass change rate estimate in the LAS of 2004–2008 from the GRACE, ICESat and CHINARE data is 5.41 ± 4.59 Gt a−1, indicating a balanced to slightly positive mass trend. Along with other published results, this suggests that a longer-term positive mass trend in the LAS may have slowed in recent years.

On April 25, 2015, a massive 8.1-magnitude earthquake struck Nepal at 2:11 pm (Beijing time). The 68-member-strong China International Search & Rescue Team (CISAR) left for Nepal at 6 am, April 26, to help with relief work. The CISAR was the first foreign team to rescue a survivor who was trapped beneath the rubble in the Gongabu area after the earthquake. On May 8, the team fulfilled the search-and-rescue mission and returned to Beijing. During the 2 weeks of rescue work, the team treated more than 3700 victims and cleared approximately 430 buildings. In this rescue mission, 10 experienced medical officers (including nine doctors and a nurse) from the General Hospital of Chinese People’s Armed Police Force (PAP) comprised the medical team of CISAR. In this report, we focus on the medical rescues by CISAR and discuss the characteristics of the medical rescue in Nepal. (Disaster Med Public Health Preparedness. 2016;page 1 of 3)

A correction method for linear hardening materials is brought forward to obtain the true (or nearly true) modulus of cylindrical specimens with middle aspect ratios in compression tests. By considering the stress concentration near the interface between the specimen and the compression platen caused by slanted contact, a “sandwich” model is developed. A correction formula is derived and all parameters can be obtained from the tested stress–strain curve. Experiments were performed on Al 2024 specimens with four aspect ratios. The corrected results are closer to the intrinsic modulus than the tested values, which verify the effectiveness of the correction method.

Twitter is a social network with over 250 million active users who collectively generate more than 500 million tweets each day. In social sciences research, Twitter has earned the focus of extensive research largely due to its openness in sharing its public data. Twitter exposes an extensive application programming interfaces (APIs) that can be used to collect a wealth of social data. In this chapter, we introduce these APIs and discuss how they can be used to conduct social sciences research. We also outline some issues that arise when using these APIs, and some strategies for collecting datasets that can give insight into a particular event.

Introduction

Twitter is a rich data source that provides several forms of information generated through the interaction of its users. These data can be harnessed to accomplish a variety of personalization and prediction tasks. Recently, Twitter data have been used to predict things as diverse as election results (Tumasjan et al., 2010; c.f. Chapter 2) or the location of earthquakes (Sakaki et al., 2010; c.f. Chapter 6). Twitter currently has over 250 million active users who collectively generate more than 500 million tweets each day. This creates a unique opportunity to conduct large-scale studies on user behavior. An important step before conducting such studies is the identification and collection of data relevant to the problem.

Twitter is an online social networking platform where the registered users can create connections and share messages with other users. Messaging on Twitter is unique, as messages are required to be at most 140 characters long, and these messages are normally broadcast to all the users on Twitter. Thus, the platform provides an avenue to share content with a large and diverse population with few resources. These interactions generate different kinds of information. Information is made accessible to the public via APIs or interfaces where requests for data can be submitted. In this chapter, we introduce different forms of Twitter data and illustrate the capabilities and restrictions imposed by the API on Twitter data analysis.

In the present study, we investigated whether high dietary Ca and exogenous parathyroid hormone 1–34 fragments (PTH 1–34) have synergistic effects on bone formation in adult mice, and explored the related mechanisms. Adult male mice were fed a normal diet, a high-Ca diet, a PTH-treated diet, or a high-Ca diet combined with subcutaneously injected PTH 1–34 (80 μg/kg per d) for 4 weeks. Bone mineral density, trabecular bone volume, osteoblast number, alkaline phosphatase (ALP)- and type I collagen-positive areas, and the expression levels of osteoblastic bone formation-related genes and proteins were increased significantly in mice fed the high-Ca diet, the PTH-treated diet, and, even more dramatically, the high-Ca diet combined with PTH. Osteoclast number and surface and the ratio of receptor activator for nuclear factor-κB ligand (RANKL):osteoprotegerin (OPG) were decreased in the high-Ca diet treatment group, increased in the PTH treatment group, but not in the combined treatment group. Furthermore, third-passage osteoblasts were treated with high Ca (5 mm), PTH 1–34 (10− 8m) or high Ca combined with PTH 1–34. Osteoblast viability and ALP activity were increased in either the high Ca-treated or PTH-treated cultures and, even more dramatically, in the cultures treated with high Ca plus PTH, with consistent up-regulation of the expression levels of osteoblast proliferation and differentiation-related genes and proteins. These results indicate that dietary Ca and PTH play synergistic roles in promoting osteoblastic bone formation by stimulating osteoblast proliferation and differentiation.

The family of interferon-inducible transmembrane proteins (IFITMs) plays a crucial role in inhibiting proliferation, promoting homotypic cell adhesion and mediating germ cell development. In the present study, the full-length cDNAs of zebrafish ifitm1 (744 bp) and ifitm3 (702 bp) were obtained by rapid amplification of cDNA ends (RACE). Reverse transcription polymerase chain reaction (RT-PCR) analysis showed that ifitm1 mRNA was expressed in the ovary, testis, brain, muscle, liver and kidney, while ifitm3 mRNA was only detected in the ovary. Based on in situ hybridization, ifitm1 mRNA was found to be strongly expressed in the ooplasm from stage I to stage II and ifitm3 mRNA was also strongly expressed in the ooplasm from stage I to stage II, furthermore ifitm3 expression ultimately localized to the cortex region beneath the plasma membrane of stage IV oocytes. During development, ifitm1 expression was initially detected in the enveloping layer cells and deep layer cells of shield stage embryos. Then, throughout the segmentation phase (10.25–24 hours post-fertilization (hpf)), ifitm1 expression was mainly detected in the head, trunk and tail regions. Unlike ifitm1, ifitm3 expression was initially detected in sphere stage embryos and was then broadly expressed throughout the embryo from the 70% epiboly stage to 24 hpf. Interestingly, ifitm3 was also expressed in primordial germ cells (PGCs) from the bud stage to 24 hpf. This expression analysis indicates that zebrafish ifitm1 may play a critical role in early organogenesis and may perform immune or hematopoietic functions and ifitm3 might be necessary for PGC migration and the formation of female germ cells.

Social forces connect individuals in different ways. When individuals get connected, one can observe distinguishable patterns in their connectivity networks. One such pattern is assortativity, also known as social similarity. In networks with assortativity, similar nodes are connected to one another more often than dissimilar nodes. For instance, in social networks, a high similarity between friends is observed. This similarity is exhibited by similar behavior, similar interests, similar activities, and shared attributes such as language, among others. In other words, friendship networks are assortative. Investigating assortativity patterns that individuals exhibit on social media helps one better understand user interactions. Assortativity is the most commonly observed pattern among linked individuals. This chapter discusses assortativity along with principal factors that result in assortative networks.

Many social forces induce assortative networks. Three common forces are influence, homophily, and confounding. Influence is the process by which an individual (the influential) affects another individual such that the influenced individual becomes more similar to the influential figure. Homophily is observed in already similar individuals. It is realized when similar individuals become friends due to their high similarity. Confounding is the environment's effect on making individuals similar. For instance, individuals who live in Russia speak Russian fluently because of the environment and are therefore similar in language. The confounding force is an external factor that is independent of inter-individual interactions and is therefore not discussed further.

In May 2011, Facebook had 721 million users, represented by a graph of 721 million nodes. A Facebook user at the time had an average of 190 friends; that is, all Facebook users, taken into account, had a total of 68.5 billion friendships (i.e., edges). What are the principal underlying processes that help initiate these friendships? More importantly, how can these seemingly independent friendships form this complex friendship network?

In social media, many social networks contain millions of nodes and billions of edges. These complex networks have billions of friendships, the reasons for existence of most of which are obscure. Humbled by the complexity of these networks and the difficulty of independently analyzing each one of these friendships, we can design models that generate, on a smaller scale, graphs similar to real-world networks. On the assumption that these models simulate properties observed in real-world networks well, the analysis of real-world networks boils down to a cost-efficient measuring of different properties of simulated networks. In addition, these models

• allow for a better understanding of phenomena observed in real-world networks by providing concrete mathematical explanations and

• allow for controlled experiments on synthetic networks when real-world networks are not available.

We discuss three principal network models in this chapter: the random graph model, the small-world model, and the preferential attachment model. These models are designed to accurately model properties observed in real-world networks. Before we delve into the details of these models, we discuss their properties.

In November 2010, a team of Dutch law enforcement agents dismantled a community of 30 million infected computers across the globe that were sending more than 3.6 billion daily spam mails. These distributed networks of infected computers are called botnets. The community of computers in a botnet transmit spam or viruses across the web without their owner's permission. The members of a botnet are rarely known; however, it is vital to identify these botnet communities and analyze their behavior to enhance internet security. This is an example of community analysis. In this chapter, we discuss community analysis in social media.

Also known as groups, clusters, or cohesive subgroups, communities have been studied extensively in many fields and, in particular, the social sciences. In social media mining, analyzing communities is essential. Studying communities in social media is important for many reasons. First, individuals often form groups based on their interests, and when studying individuals, we are interested in identifying these groups. Consider the importance of finding groups with similar reading tastes by an online book seller for recommendation purposes. Second, groups provide a clear global view of user interactions, whereas a local-view of individual behavior is often noisy and ad hoc. Finally, some behaviors are only observable in a group setting and not on an individual level. This is because the individual's behavior can fluctuate, but group collective behavior is more robust to change. Consider the interactions between two opposing political groups on social media. Two individuals, one from each group, can hold similar opinions on a subject, but what is important is that their communities can exhibit opposing views on the same subject.